People high in this trait are likable since they are approachable and do not have a judgmental attitude. They also practice active listening and make the people around them feel seen and important. Tolerance: People who score high in this trait are often curious about and interested in others – showing tolerance and understanding, which in turn makes them interesting to others. Moreover, such people tend to show positive body language such as a strong posture, smiling, and having open shoulders. This trait also covers lavishing others with positive and encouraging comments. The test provides feedback such as the following: Positivity: Optimistic people make others feel empowered and happy they positively affect those around them since they can find something good in most people and situations. The IDR-LPT is not associated with any specific researchers in the field of personality psychology, social psychology, or any affiliated research institutions.
Stephen Reysen, Ph.D., who created the Reysen Likability Scale. Her notes are detailed and organized and her preparation for each class is extensive.The IDRlabs Likable Person Test (IDR-LPT) was developed by IDRlabs. Tasha goes to great lengths to make information accessible to quantitative methods and non-quantitative methods students. Everything was well organized and clearly presented. I cannot say enough good things about Tasha's teaching and this course. This class really covered an enormous amount, and she provided incredibly detailed and accessible material with the online format. Beretvas is the epitome of teaching excellence – she is extremely knowledgeable in her area and is able to explain complex concepts to learners with a variety of backgrounds and clear cares about teaching. A sample of participant comments is provided below.ĭr. Meta-Analysis of Treatment Effects on Dichotomous OutcomesĨ.2 Meta-Regression of Categorical Effect SizesĪlthough this is the first time Tasha will be teaching this class with our organization, her previous classes on this topic at other venues have generated enthusiastic reviews. Missing Data and Publication Biasħ.2 Handling Within-Study Dependence in Correlation Estimatesħ.3 Testing publication bias in meta-analysis of CorrelationsĬhapter 8. Handling Within-Study Dependence in SMDsĥ.2 Using GLS to handle within-study dependence & moderationĥ.3 Robust Variance Estimation of Meta-Regression ModelsĬhapter 6. Pooling Standardized Mean Differencesģ.1 Fixed-Effects Pooling of Effect Size Estimatesģ.2 Random-Effects Pooling of Effect Size Estimatesģ.3 Demonstration of Pooling SMDs by hand and with escalcĤ.2 Using rma to estimate fixed- & mixed-effects meta-regression modelsĬhapter 5. Calculating the Standardized Mean DifferenceĢ.1 Calculating the Standardized Mean Difference and its VarianceĢ.2 Transforming Inferential Statistics to Obtain the SMDĢ.3 Calculating the SMD from repeated measures design dataĬhapter 3.
#Define meta analysis in psychology how to#
We will also learn how to assess and correct for potential publication bias.ġ.1 Introduction to Quantitative Meta-AnalysisĬhapter 2.
Meta-analytic data are complicated and we will cover how best to handle some of the methodological complexities that are encountered. We will also learn how to average the effect size estimates across primary studies and how to conduct moderator analyses. In this workshop, we will learn how to calculate the most common types of effect sizes (the standardized mean difference, correlation coefficient and log-odds ratio) given the different kinds of descriptive and inferential statistics that are reported. Primary study characteristics can then be investigated as sources of variability in effect size estimates through the use of meta-analytic moderator analyses. The procedure entails first capturing effect sizes that numerically describe the relationship among relevant variables in each primary study. Quantitative meta-analysis allows synthesis of results from primary studies investigating relations among common variables. Introduction to Quantitative Meta-Analysis is a four-day workshop focused on the statistical techniques used to conduct quantitative meta-analyses.
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